RNA-seq for Cancer Research: Top 5 Projects for Your Portfolio

Cancer research has entered a data-intensive era. The ability to understand how genes behave under healthy and disease conditions—especially at the transcriptome level—has become critical. RNA-sequencing (RNA-seq) is now one of the most powerful tools to study cancer, enabling researchers to analyze gene expression patterns, identify potential biomarkers, and understand the molecular mechanisms driving tumor development.

For students, early-career researchers, and those entering the bioinformatics field, hands-on projects in RNA-seq are not just learning exercises—they’re portfolio builders. A well-structured project built on real datasets can demonstrate your practical skills in running pipelines, analyzing differential gene expression, and interpreting biological meaning from raw reads.

This blog outlines five impactful RNA-seq projects tailored specifically for cancer research. Each is ideal to practice core skills like running an RNA-seq pipeline tutorial, understanding single-cell workflows, and following a DESeq2 step-by-step guide for robust differential gene expression analysis.

Project 1: Differential Expression in Breast Cancer Subtypes

Breast cancer has several molecular subtypes such as Luminal A, Luminal B, HER2-enriched, and Basal-like. Each subtype responds differently to therapy and has distinct gene expression profiles.

In this project, you can download publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) or GEO and perform a differential gene expression analysis between two subtypes, such as HER2+ and Basal-like. The DESeq2 step-by-step guide can help you perform normalization, filtering, and statistical testing to identify significantly upregulated and downregulated genes.

This project demonstrates your ability to link bioinformatics results with clinical relevance—an essential skill in translational cancer research.

Project 2: Identifying Biomarkers in Lung Cancer Using RNA-seq

This project focuses on one of the most clinically significant applications of RNA-seq: discovering novel cancer biomarkers.

Start by selecting RNA-seq datasets comparing lung tumor samples and adjacent normal tissues. Your goal will be to identify RNA-seq for cancer biomarkers—genes that show consistent and significant expression differences in tumors and may serve as diagnostic or prognostic indicators.

This project involves end-to-end RNA-seq pipeline steps, from quality control and trimming to alignment and expression quantification. It’s a good choice to apply what you’ve learned from any RNA-seq pipeline tutorial and to focus on data interpretation, not just tool execution.

Project 3: Exploring Immune Signatures in Tumor Microenvironment Using Single-Cell RNA-seq

The tumor microenvironment is rich with different immune and stromal cells that influence cancer progression and response to immunotherapy. Single-cell RNA-seq allows researchers to study these individual cells in detail.

In this project, you will use single-cell datasets from melanoma or colorectal cancer to explore how immune cell populations—like T-cells, macrophages, and dendritic cells—behave within tumors. A good single-cell RNA-seq course or tutorial will guide you through preprocessing, dimensionality reduction, clustering, and marker gene identification.

This type of project is extremely valuable if you plan to enter immuno-oncology or precision medicine research, as it shows your ability to handle more complex, high-resolution transcriptomic data.

Project 4: Time-Series Analysis of Gene Expression During Cancer Treatment

Time-course RNA-seq experiments provide insights into how gene expression evolves under treatment. For example, studying how a cancer cell line responds to a chemotherapy agent over time can uncover drug-resistance genes or early-response pathways.

Choose a dataset where cancer cells were treated over several time points (e.g., 0h, 6h, 12h, 24h) and apply RNA-seq workflows to examine trends. Use tools like DESeq2 or edgeR to model time-course effects.

This project strengthens your statistical modeling skills and demonstrates your ability to work with longitudinal transcriptomic data—a skill sought in pharmaceutical and clinical research environments.

Project 5: lncRNA and mRNA Co-expression Network in Prostate Cancer

Long non-coding RNAs (lncRNAs) play critical roles in cancer regulation but are often overlooked in basic RNA-seq pipelines.

In this project, you’ll analyze both coding and non-coding transcriptome data from prostate cancer patients. After performing expression analysis, construct a co-expression network to identify lncRNAs that may be co-regulated with key oncogenes or tumor suppressor genes.

This kind of integrative analysis pushes your skill set beyond basic gene expression analysis and toward systems biology—highlighting your ability to draw connections across gene networks, not just individual genes.

Final Thoughts: Your Pathway from Pipeline to Publication

Each of these projects builds a key area of expertise: mastering a full RNA-seq pipeline, interpreting differential expression, working with single-cell data, modeling

 time-dependent changes, and integrating coding/non-coding RNAs. If you’re following a RNA-seq pipeline tutorial, pairing it with one of these research ideas will give you a practical edge.

Whether you're attending a structured gene expression microarray course or shifting toward RNA-seq analysis, building a portfolio with clear biological questions and curated datasets is the best way to turn theory into demonstrable skill.

For students aiming to publish or contribute to real-world cancer studies, learning how to normalize and interpret data is as important as running the tools. These projects not only strengthen your bioinformatics foundation but also make your profile competitive for internships, research positions, and postgraduate programs.

Start small, pick one project that excites you the most, and build from there. The cancer research world needs more skilled data scientists—and this is how you begin.

Conclusion

RNA-seq has revolutionized the way we understand cancer biology by offering deep insights into gene activity at an unprecedented scale. By working on the five curated project ideas discussed above, you not only gain practical exposure to the entire RNA-seq analysis pipeline but also strengthen your ability to interpret data in a biologically meaningful way.

From identifying cancer biomarkers and exploring immune profiles using single-cell RNA-seq, to modeling gene expression over time and examining regulatory lncRNA-mRNA interactions—each project equips you with essential, real-world skills. Whether you're following a structured RNA-seq pipeline tutorial, diving into a DESeq2 step-by-step guide, or exploring advanced data from a single-cell RNA-seq course, these experiences contribute directly to your portfolio and professional growth.

Ultimately, the more confidently you can analyze, interpret, and present RNA-seq data in the context of cancer research, the better prepared you’ll be to contribute to high-impact scientific questions—and to differentiate yourself in the competitive field of bioinformatics.